Solder joints inspection using neural network and fuzzy rule-based classification

In this paper, we described an approach to automation of visual inspection of solder joint defects of SMC (surface mounted components) on PCBs (Printed Circuit Board) by using neural network and fuzzy rule-based classification method. Inherently, surface of the solder joints is curved, tiny and specular reflective; it induces a difficulty of taking good image of the solder joints. And the shape of the solder joints tends to greatly vary with soldering condition and the shapes are not identical each other, even though solder joints belong to a set of the same soldering quality. This problem makes it difficult to classification the solder joints according to their qualities. Neural network and fuzzy rule-based classification method is proposed to efficiently make human-like classification criteria of the solder joint shapes. The performance of the proposed approach is tested on numerous samples of commercial computer PCB board and compared with the human inspector performance.